A Multiform Heterogeneity Framework for Alzheimer's Disease Based on Multimodal Neuroimaging

Understanding the heterogeneity of Alzheimer's disease (AD) is crucial for advancing precision medicine specifically tailored to this disorder. Recent research has deepened our understanding of AD heterogeneity; however, translating these insights from bench to bedside via neuroimaging heterogeneity frameworks presents significant challenges. In this review, we systematically revisit prior studies and summarize the existing methodology of data-driven neuroimaging studies for AD heterogeneity. We organized the current methodology into 1) a subtyping clustering strategy for patients with AD, and we also subdivided it into subtyping analysis based on cross-sectional multimodal neuroimaging profiles and the identification of long-term disease progression from short-term datasets; 2) a stratified strategy that integrates neuroimaging measures with biomarkers; and 3) individual-specific abnormal patterns based on the normative model. Then, we evaluated the characteristics of these studies along 2 dimensions: 1) the understanding of pathology and 2) clinical application. We systematically address the limitations, challenges, and future directions of research into AD heterogeneity. Our goal is to enhance the neuroimaging heterogeneity framework for AD, thereby facilitating its transition from bench to bedside.